Field
Embodiments of the present invention generally relate to a system and method to manage customer-resource communications and particularly to a system and method for managing customer-resource communications in real time.
Description of Related Art
Contact centers are employed by many enterprises to service, inbound and outbound contacts or customers. A primary objective of contact center management is to ultimately maximize contact center performance and profitability. An ongoing challenge in contact center administration is monitoring and optimizing contact center efficiency usage of its available resources. The contact center efficiency is generally measured by metrics such as Service Level Agreement (SLA), Customer Satisfaction (CSAT), and match rate.
Match rate is another indicator used in measuring the contact center efficiency. Match rate is usually determined by dividing the number of contacts accepted by a primary skill level resource within a period of time by the number of contacts accepted by any resource in a queue over the same period. A resource with a primary skill level is one who typically may handle contacts of a certain nature more effectively and/or efficiently as compared to a resource of lesser skill level. There are other contact center resources that may not be as proficient as the primary skill level resource, and those resources are identified either as skill level resources or backup skill level resources. As can be appreciated, contacts received by a primary skill level resource are typically handled more quickly and accurately or effectively (e.g., higher revenue attained) than a contact received by a secondary or even backup skill level resource. Thus, it is an objective of most contact centers to optimize match rate along with the service level.
The contact center also has to maintain the Customer Satisfaction (CSAT) metrics. For this purpose, resources may have to maintain the quality of services provided to the customers through multimedia (e.g., voice contacts, video contacts, emails, etc.). Providing an optimum quality of service is majorly governed by interactions or conversations between a customer and a resource. These involve conversation styles, facial expressions, personality types of individuals etc. A well known method for defining personality types includes Myers Briggs Type Indicator (MBTI). An example could include a thinking-type and a feeling-type personality, where a thinking-type makes a decision based on facts while a feeling-type makes a decision on a situation-to-situation basis or applies a best fit approach.
Generally, contact centers deploy an Automatic Call Distributer (ACD) to route incoming calls from customers to resources. The operation of the ACD involves various routing algorithms to match a customer with a resource. A commonly used routing algorithm is behavioral routing, where incoming calls from the customers are routed to resources in a contact center based on behavior and personalities of the customers known from past experiences or pre-stored previous interactions with the contact center resources. Situations often exist where an appropriate resource complementing the personality of the customer is not available based on behavioral routing and the call is routed to another resource using a different routing algorithm. Then a problem arises as the customer having a personality type is connected to a resource proficient in handling customers of a different personality type. The resource is then not able to handle the conversation in an efficient manner and needs assistance in responding to queries of the customer according to the customer's personality or conversational style.
Few technologies exist that assist resources in preparing responses to customer queries. However, these technologies merely take into account only contextual parameters of a query. For example, such technologies identify key words from a customer query and suggest possible pre-defined solutions to the resources. These technologies fail to take into account behavioral characteristics or personality types of the customer. For example, words spoken or written in a conversation by a customer may reflect a current behavior such as angry, frustrated, irritated or happy, satisfied, etc. Also, there may be several behavioral turns in a conversation between a customer and a resource. An example of such a behavioral turn would be from a state of agreement to a state of disagreement leading to an argument. Therefore, it becomes essential to monitor the customer's behavior during conversations to achieve customer satisfaction and desired business goals.
Therefore, a need exists for an efficient way of handling customer-resource interactions to achieve enterprise business goals.